Overview

Dataset statistics

Number of variables14
Number of observations5002
Missing cells1816
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory665.9 B

Variable types

DateTime1
Categorical5
Numeric8

Alerts

Ruta has a high cardinality: 4119 distinct valuesHigh cardinality
OperadOR has a high cardinality: 2267 distinct valuesHigh cardinality
route has a high cardinality: 3837 distinct valuesHigh cardinality
ac_type has a high cardinality: 2462 distinct valuesHigh cardinality
summary has a high cardinality: 4851 distinct valuesHigh cardinality
all_aboard is highly overall correlated with PASAJEROS A BORDO and 3 other fieldsHigh correlation
PASAJEROS A BORDO is highly overall correlated with all_aboard and 3 other fieldsHigh correlation
crew_aboard is highly overall correlated with all_aboard and 3 other fieldsHigh correlation
cantidad de fallecidos is highly overall correlated with all_aboard and 4 other fieldsHigh correlation
passenger_fatalities is highly overall correlated with all_aboard and 2 other fieldsHigh correlation
crew_fatalities is highly overall correlated with crew_aboard and 1 other fieldsHigh correlation
route has 759 (15.2%) missing valuesMissing
PASAJEROS A BORDO has 219 (4.4%) missing valuesMissing
crew_aboard has 217 (4.3%) missing valuesMissing
passenger_fatalities has 233 (4.7%) missing valuesMissing
crew_fatalities has 233 (4.7%) missing valuesMissing
summary has 59 (1.2%) missing valuesMissing
ground is highly skewed (γ1 = 48.95716288)Skewed
Ruta is uniformly distributedUniform
summary is uniformly distributedUniform
PASAJEROS A BORDO has 866 (17.3%) zerosZeros
cantidad de fallecidos has 76 (1.5%) zerosZeros
passenger_fatalities has 1037 (20.7%) zerosZeros
crew_fatalities has 398 (8.0%) zerosZeros
ground has 4710 (94.2%) zerosZeros

Reproduction

Analysis started2023-05-23 15:31:15.881832
Analysis finished2023-05-23 15:31:38.144324
Duration22.26 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

fecha
Date

Distinct4571
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum1915-03-05 00:00:00
Maximum2021-07-06 00:00:00
2023-05-23T12:31:38.650237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:38.974345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ruta
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct4119
Distinct (%)82.4%
Missing5
Missing (%)0.1%
Memory size419.8 KiB
Moscow, Russia
 
16
Manila, Philippines
 
15
New York, New York
 
14
Cairo, Egypt
 
13
Sao Paulo, Brazil
 
13
Other values (4114)
4926 

Length

Max length72
Median length49
Mean length20.808685
Min length5

Characters and Unicode

Total characters103981
Distinct characters90
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3683 ?
Unique (%)73.7%

Sample

1st rowTienen, Belgium
2nd rowOff Cuxhaven, Germany
3rd rowNear Jambol, Bulgeria
4th rowBillericay, England
5th rowPotters Bar, England

Common Values

ValueCountFrequency (%)
Moscow, Russia 16
 
0.3%
Manila, Philippines 15
 
0.3%
New York, New York 14
 
0.3%
Cairo, Egypt 13
 
0.3%
Sao Paulo, Brazil 13
 
0.3%
Bogota, Colombia 12
 
0.2%
Rio de Janeiro, Brazil 12
 
0.2%
Chicago, Illinois 11
 
0.2%
Near Moscow, Russia 11
 
0.2%
Tehran, Iran 10
 
0.2%
Other values (4109) 4870
97.4%

Length

2023-05-23T12:31:39.332281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
near 1349
 
9.2%
off 350
 
2.4%
russia 255
 
1.7%
new 228
 
1.6%
brazil 176
 
1.2%
colombia 153
 
1.0%
canada 130
 
0.9%
france 126
 
0.9%
california 117
 
0.8%
mexico 113
 
0.8%
Other values (4150) 11636
79.5%

Most occurring characters

ValueCountFrequency (%)
a 13024
 
12.5%
9689
 
9.3%
e 7062
 
6.8%
i 6555
 
6.3%
n 6538
 
6.3%
r 6022
 
5.8%
o 5362
 
5.2%
, 5204
 
5.0%
l 3997
 
3.8%
s 3525
 
3.4%
Other values (80) 37003
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74010
71.2%
Uppercase Letter 14718
 
14.2%
Space Separator 9690
 
9.3%
Other Punctuation 5351
 
5.1%
Dash Punctuation 103
 
0.1%
Decimal Number 66
 
0.1%
Control 21
 
< 0.1%
Open Punctuation 11
 
< 0.1%
Close Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13024
17.6%
e 7062
9.5%
i 6555
8.9%
n 6538
8.8%
r 6022
 
8.1%
o 5362
 
7.2%
l 3997
 
5.4%
s 3525
 
4.8%
t 3103
 
4.2%
u 2753
 
3.7%
Other values (31) 16069
21.7%
Uppercase Letter
ValueCountFrequency (%)
N 2029
13.8%
C 1453
 
9.9%
S 1144
 
7.8%
M 998
 
6.8%
B 951
 
6.5%
A 919
 
6.2%
P 787
 
5.3%
I 720
 
4.9%
R 652
 
4.4%
O 586
 
4.0%
Other values (17) 4479
30.4%
Decimal Number
ValueCountFrequency (%)
0 24
36.4%
1 15
22.7%
2 9
 
13.6%
5 8
 
12.1%
8 3
 
4.5%
3 2
 
3.0%
7 2
 
3.0%
9 2
 
3.0%
6 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 5204
97.3%
. 115
 
2.1%
' 24
 
0.4%
/ 6
 
0.1%
& 1
 
< 0.1%
: 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
9689
> 99.9%
  1
 
< 0.1%
Control
ValueCountFrequency (%)
16
76.2%
5
 
23.8%
Dash Punctuation
ValueCountFrequency (%)
- 103
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88728
85.3%
Common 15253
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13024
14.7%
e 7062
 
8.0%
i 6555
 
7.4%
n 6538
 
7.4%
r 6022
 
6.8%
o 5362
 
6.0%
l 3997
 
4.5%
s 3525
 
4.0%
t 3103
 
3.5%
u 2753
 
3.1%
Other values (58) 30787
34.7%
Common
ValueCountFrequency (%)
9689
63.5%
, 5204
34.1%
. 115
 
0.8%
- 103
 
0.7%
0 24
 
0.2%
' 24
 
0.2%
16
 
0.1%
1 15
 
0.1%
( 11
 
0.1%
) 11
 
0.1%
Other values (12) 41
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103939
> 99.9%
None 42
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13024
 
12.5%
9689
 
9.3%
e 7062
 
6.8%
i 6555
 
6.3%
n 6538
 
6.3%
r 6022
 
5.8%
o 5362
 
5.2%
, 5204
 
5.0%
l 3997
 
3.8%
s 3525
 
3.4%
Other values (63) 36961
35.6%
None
ValueCountFrequency (%)
é 14
33.3%
ö 5
 
11.9%
ó 4
 
9.5%
í 4
 
9.5%
á 2
 
4.8%
ï 2
 
4.8%
è 1
 
2.4%
ô 1
 
2.4%
à 1
 
2.4%
ä 1
 
2.4%
Other values (7) 7
16.7%

OperadOR
Categorical

Distinct2267
Distinct (%)45.4%
Missing9
Missing (%)0.2%
Memory size412.1 KiB
Aeroflot
 
253
Military - U.S. Air Force
 
141
Air France
 
74
Deutsche Lufthansa
 
63
United Air Lines
 
44
Other values (2262)
4418 

Length

Max length65
Median length47
Mean length18.958342
Min length3

Characters and Unicode

Total characters94659
Distinct characters87
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1734 ?
Unique (%)34.7%

Sample

1st rowMilitary - German Navy
2nd rowMilitary - German Navy
3rd rowMilitary - German Army
4th rowMilitary - German Navy
5th rowMilitary - German Navy

Common Values

ValueCountFrequency (%)
Aeroflot 253
 
5.1%
Military - U.S. Air Force 141
 
2.8%
Air France 74
 
1.5%
Deutsche Lufthansa 63
 
1.3%
United Air Lines 44
 
0.9%
Military - U.S. Army Air Forces 43
 
0.9%
China National Aviation Corporation 43
 
0.9%
Pan American World Airways 41
 
0.8%
American Airlines 37
 
0.7%
US Aerial Mail Service 35
 
0.7%
Other values (2257) 4219
84.3%

Length

2023-05-23T12:31:40.059152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
air 1481
 
10.3%
957
 
6.7%
airlines 840
 
5.8%
military 774
 
5.4%
force 557
 
3.9%
airways 453
 
3.2%
u.s 300
 
2.1%
aeroflot 265
 
1.8%
lines 184
 
1.3%
royal 152
 
1.1%
Other values (2079) 8415
58.5%

Most occurring characters

ValueCountFrequency (%)
i 10203
 
10.8%
9409
 
9.9%
r 8841
 
9.3%
a 7776
 
8.2%
e 6777
 
7.2%
n 5526
 
5.8%
A 5082
 
5.4%
o 4380
 
4.6%
l 4075
 
4.3%
s 4000
 
4.2%
Other values (77) 28590
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 68125
72.0%
Uppercase Letter 15056
 
15.9%
Space Separator 9410
 
9.9%
Dash Punctuation 935
 
1.0%
Other Punctuation 865
 
0.9%
Open Punctuation 115
 
0.1%
Close Punctuation 115
 
0.1%
Decimal Number 30
 
< 0.1%
Control 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 10203
15.0%
r 8841
13.0%
a 7776
11.4%
e 6777
9.9%
n 5526
8.1%
o 4380
6.4%
l 4075
 
6.0%
s 4000
 
5.9%
t 3916
 
5.7%
c 1996
 
2.9%
Other values (28) 10635
15.6%
Uppercase Letter
ValueCountFrequency (%)
A 5082
33.8%
M 1213
 
8.1%
S 1136
 
7.5%
C 910
 
6.0%
F 901
 
6.0%
T 679
 
4.5%
L 661
 
4.4%
U 532
 
3.5%
P 512
 
3.4%
N 493
 
3.3%
Other values (16) 2937
19.5%
Decimal Number
ValueCountFrequency (%)
0 5
16.7%
7 4
13.3%
4 4
13.3%
2 3
10.0%
5 3
10.0%
1 3
10.0%
8 2
 
6.7%
6 2
 
6.7%
9 2
 
6.7%
3 2
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 714
82.5%
/ 109
 
12.6%
' 25
 
2.9%
, 10
 
1.2%
& 6
 
0.7%
? 1
 
0.1%
Space Separator
ValueCountFrequency (%)
9409
> 99.9%
  1
 
< 0.1%
Control
ValueCountFrequency (%)
6
75.0%
2
 
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 935
100.0%
Open Punctuation
ValueCountFrequency (%)
( 115
100.0%
Close Punctuation
ValueCountFrequency (%)
) 115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83181
87.9%
Common 11478
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 10203
12.3%
r 8841
 
10.6%
a 7776
 
9.3%
e 6777
 
8.1%
n 5526
 
6.6%
A 5082
 
6.1%
o 4380
 
5.3%
l 4075
 
4.9%
s 4000
 
4.8%
t 3916
 
4.7%
Other values (54) 22605
27.2%
Common
ValueCountFrequency (%)
9409
82.0%
- 935
 
8.1%
. 714
 
6.2%
( 115
 
1.0%
) 115
 
1.0%
/ 109
 
0.9%
' 25
 
0.2%
, 10
 
0.1%
6
 
0.1%
& 6
 
0.1%
Other values (13) 34
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94536
99.9%
None 123
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 10203
 
10.8%
9409
 
10.0%
r 8841
 
9.4%
a 7776
 
8.2%
e 6777
 
7.2%
n 5526
 
5.8%
A 5082
 
5.4%
o 4380
 
4.6%
l 4075
 
4.3%
s 4000
 
4.2%
Other values (64) 28467
30.1%
None
ValueCountFrequency (%)
é 102
82.9%
á 5
 
4.1%
à 2
 
1.6%
í 2
 
1.6%
ó 2
 
1.6%
ç 2
 
1.6%
ï 2
 
1.6%
ã 1
 
0.8%
ú 1
 
0.8%
ê 1
 
0.8%
Other values (3) 3
 
2.4%

route
Categorical

HIGH CARDINALITY  MISSING 

Distinct3837
Distinct (%)90.4%
Missing759
Missing (%)15.2%
Memory size393.6 KiB
Training
 
96
Sightseeing
 
31
Test flight
 
22
Sao Paulo - Rio de Janeiro
 
7
Test
 
6
Other values (3832)
4081 

Length

Max length59
Median length51
Mean length22.174169
Min length4

Characters and Unicode

Total characters94085
Distinct characters92
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3630 ?
Unique (%)85.6%

Sample

1st rowShuttle
2nd rowVenice Taliedo
3rd rowParis - Hounslow
4th rowWashington - Newark
5th rowLondon - Paris

Common Values

ValueCountFrequency (%)
Training 96
 
1.9%
Sightseeing 31
 
0.6%
Test flight 22
 
0.4%
Sao Paulo - Rio de Janeiro 7
 
0.1%
Test 6
 
0.1%
Rio de Janeiro - Sao Paulo 5
 
0.1%
Villavicencio - Mitu 4
 
0.1%
Bogota - Barranquilla 4
 
0.1%
Barranquilla - Bogota 4
 
0.1%
Croydon - Paris 4
 
0.1%
Other values (3827) 4060
81.2%
(Missing) 759
 
15.2%

Length

2023-05-23T12:31:40.564065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4633
27.5%
city 213
 
1.3%
new 149
 
0.9%
san 140
 
0.8%
york 117
 
0.7%
paris 116
 
0.7%
training 103
 
0.6%
de 101
 
0.6%
london 88
 
0.5%
moscow 84
 
0.5%
Other values (3628) 11078
65.9%

Most occurring characters

ValueCountFrequency (%)
12644
 
13.4%
a 9832
 
10.5%
n 5567
 
5.9%
o 5501
 
5.8%
i 5241
 
5.6%
e 5106
 
5.4%
- 4927
 
5.2%
r 4485
 
4.8%
l 3419
 
3.6%
s 3072
 
3.3%
Other values (82) 34291
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 62450
66.4%
Uppercase Letter 12939
 
13.8%
Space Separator 12645
 
13.4%
Dash Punctuation 4931
 
5.2%
Other Punctuation 1065
 
1.1%
Control 30
 
< 0.1%
Decimal Number 16
 
< 0.1%
Final Punctuation 4
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9832
15.7%
n 5567
 
8.9%
o 5501
 
8.8%
i 5241
 
8.4%
e 5106
 
8.2%
r 4485
 
7.2%
l 3419
 
5.5%
s 3072
 
4.9%
t 3002
 
4.8%
u 2566
 
4.1%
Other values (30) 14659
23.5%
Uppercase Letter
ValueCountFrequency (%)
C 1232
 
9.5%
B 1140
 
8.8%
S 1081
 
8.4%
A 1045
 
8.1%
M 1042
 
8.1%
P 823
 
6.4%
L 788
 
6.1%
T 709
 
5.5%
K 640
 
4.9%
N 631
 
4.9%
Other values (18) 3808
29.4%
Decimal Number
ValueCountFrequency (%)
9 3
18.8%
4 3
18.8%
1 3
18.8%
7 2
12.5%
2 2
12.5%
8 1
 
6.2%
6 1
 
6.2%
0 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 915
85.9%
. 98
 
9.2%
/ 20
 
1.9%
' 20
 
1.9%
? 6
 
0.6%
: 5
 
0.5%
\ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
12644
> 99.9%
  1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 4927
99.9%
– 4
 
0.1%
Control
ValueCountFrequency (%)
29
96.7%
1
 
3.3%
Final Punctuation
ValueCountFrequency (%)
’ 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75389
80.1%
Common 18696
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9832
 
13.0%
n 5567
 
7.4%
o 5501
 
7.3%
i 5241
 
7.0%
e 5106
 
6.8%
r 4485
 
5.9%
l 3419
 
4.5%
s 3072
 
4.1%
t 3002
 
4.0%
u 2566
 
3.4%
Other values (58) 27598
36.6%
Common
ValueCountFrequency (%)
12644
67.6%
- 4927
 
26.4%
, 915
 
4.9%
. 98
 
0.5%
29
 
0.2%
/ 20
 
0.1%
' 20
 
0.1%
? 6
 
< 0.1%
: 5
 
< 0.1%
’ 4
 
< 0.1%
Other values (14) 28
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93956
99.9%
None 121
 
0.1%
Punctuation 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12644
 
13.5%
a 9832
 
10.5%
n 5567
 
5.9%
o 5501
 
5.9%
i 5241
 
5.6%
e 5106
 
5.4%
- 4927
 
5.2%
r 4485
 
4.8%
l 3419
 
3.6%
s 3072
 
3.3%
Other values (63) 34162
36.4%
None
ValueCountFrequency (%)
é 38
31.4%
í 21
17.4%
á 15
 
12.4%
ó 14
 
11.6%
ü 6
 
5.0%
ã 6
 
5.0%
ç 4
 
3.3%
è 4
 
3.3%
ÃŽ 3
 
2.5%
ö 2
 
1.7%
Other values (7) 8
 
6.6%
Punctuation
ValueCountFrequency (%)
’ 4
50.0%
– 4
50.0%

ac_type
Categorical

Distinct2462
Distinct (%)49.3%
Missing13
Missing (%)0.3%
Memory size407.9 KiB
Douglas DC-3
 
333
de Havilland Canada DHC-6 Twin Otter 300
 
81
Douglas C-47A
 
70
Douglas C-47
 
64
Douglas DC-4
 
41
Other values (2457)
4400 

Length

Max length42
Median length36
Mean length18.543997
Min length4

Characters and Unicode

Total characters92516
Distinct characters77
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1857 ?
Unique (%)37.2%

Sample

1st rowZeppelin L-8 (airship)
2nd rowZeppelin L-10 (airship)
3rd rowSchutte-Lanz S-L-10 (airship)
4th rowZeppelin L-32 (airship)
5th rowZeppelin L-31 (airship)

Common Values

ValueCountFrequency (%)
Douglas DC-3 333
 
6.7%
de Havilland Canada DHC-6 Twin Otter 300 81
 
1.6%
Douglas C-47A 70
 
1.4%
Douglas C-47 64
 
1.3%
Douglas DC-4 41
 
0.8%
Antonov AN-26 35
 
0.7%
Yakovlev YAK-40 35
 
0.7%
Junkers JU-52/3m 30
 
0.6%
De Havilland DH-4 27
 
0.5%
Douglas DC-6B 27
 
0.5%
Other values (2452) 4246
84.9%

Length

2023-05-23T12:31:41.128966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
douglas 1130
 
8.3%
boeing 418
 
3.1%
dc-3 387
 
2.8%
lockheed 332
 
2.4%
de 294
 
2.2%
havilland 292
 
2.1%
antonov 288
 
2.1%
canada 159
 
1.2%
otter 146
 
1.1%
ilyushin 142
 
1.0%
Other values (2522) 10011
73.6%

Most occurring characters

ValueCountFrequency (%)
8641
 
9.3%
- 5178
 
5.6%
e 4833
 
5.2%
o 4638
 
5.0%
a 4631
 
5.0%
n 3852
 
4.2%
l 3690
 
4.0%
i 3474
 
3.8%
r 3299
 
3.6%
C 3033
 
3.3%
Other values (67) 47247
51.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46357
50.1%
Uppercase Letter 17887
 
19.3%
Decimal Number 13806
 
14.9%
Space Separator 8642
 
9.3%
Dash Punctuation 5178
 
5.6%
Other Punctuation 264
 
0.3%
Open Punctuation 188
 
0.2%
Close Punctuation 187
 
0.2%
Math Symbol 3
 
< 0.1%
Control 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4833
10.4%
o 4638
10.0%
a 4631
10.0%
n 3852
 
8.3%
l 3690
 
8.0%
i 3474
 
7.5%
r 3299
 
7.1%
s 2912
 
6.3%
t 2354
 
5.1%
u 2216
 
4.8%
Other values (18) 10458
22.6%
Uppercase Letter
ValueCountFrequency (%)
C 3033
17.0%
D 2818
15.8%
A 1901
10.6%
B 1727
9.7%
H 1016
 
5.7%
L 881
 
4.9%
F 795
 
4.4%
S 790
 
4.4%
I 639
 
3.6%
T 620
 
3.5%
Other values (16) 3667
20.5%
Decimal Number
ValueCountFrequency (%)
2 2166
15.7%
0 2103
15.2%
1 2016
14.6%
3 1706
12.4%
4 1704
12.3%
7 1494
10.8%
6 875
6.3%
5 713
 
5.2%
8 664
 
4.8%
9 365
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/ 185
70.1%
. 76
28.8%
, 2
 
0.8%
& 1
 
0.4%
Space Separator
ValueCountFrequency (%)
8641
> 99.9%
  1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 5178
100.0%
Open Punctuation
ValueCountFrequency (%)
( 188
100.0%
Close Punctuation
ValueCountFrequency (%)
) 187
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Control
ValueCountFrequency (%)
2
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64244
69.4%
Common 28272
30.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4833
 
7.5%
o 4638
 
7.2%
a 4631
 
7.2%
n 3852
 
6.0%
l 3690
 
5.7%
i 3474
 
5.4%
r 3299
 
5.1%
C 3033
 
4.7%
s 2912
 
4.5%
D 2818
 
4.4%
Other values (44) 27064
42.1%
Common
ValueCountFrequency (%)
8641
30.6%
- 5178
18.3%
2 2166
 
7.7%
0 2103
 
7.4%
1 2016
 
7.1%
3 1706
 
6.0%
4 1704
 
6.0%
7 1494
 
5.3%
6 875
 
3.1%
5 713
 
2.5%
Other values (13) 1676
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92497
> 99.9%
None 17
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8641
 
9.3%
- 5178
 
5.6%
e 4833
 
5.2%
o 4638
 
5.0%
a 4631
 
5.0%
n 3852
 
4.2%
l 3690
 
4.0%
i 3474
 
3.8%
r 3299
 
3.6%
C 3033
 
3.3%
Other values (62) 47228
51.1%
None
ValueCountFrequency (%)
é 12
70.6%
è 4
 
23.5%
  1
 
5.9%
Punctuation
ValueCountFrequency (%)
‘ 1
50.0%
’ 1
50.0%

all_aboard
Real number (ℝ)

Distinct244
Distinct (%)4.9%
Missing17
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean31.147242
Minimum0
Maximum644
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-05-23T12:31:41.485363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median16
Q335
95-th percentile117.8
Maximum644
Range644
Interquartile range (IQR)28

Descriptive statistics

Standard deviation45.499656
Coefficient of variation (CV)1.4607925
Kurtosis23.92979
Mean31.147242
Median Absolute Deviation (MAD)11
Skewness3.9190237
Sum155269
Variance2070.2187
MonotonicityNot monotonic
2023-05-23T12:31:41.761315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 280
 
5.6%
2 245
 
4.9%
4 202
 
4.0%
5 189
 
3.8%
10 179
 
3.6%
6 174
 
3.5%
7 164
 
3.3%
1 137
 
2.7%
9 130
 
2.6%
11 128
 
2.6%
Other values (234) 3157
63.1%
ValueCountFrequency (%)
0 5
 
0.1%
1 137
2.7%
2 245
4.9%
3 280
5.6%
4 202
4.0%
5 189
3.8%
6 174
3.5%
7 164
3.3%
8 119
2.4%
9 130
2.6%
ValueCountFrequency (%)
644 1
< 0.1%
524 1
< 0.1%
517 1
< 0.1%
394 1
< 0.1%
393 1
< 0.1%
384 1
< 0.1%
356 1
< 0.1%
349 1
< 0.1%
346 1
< 0.1%
340 1
< 0.1%

PASAJEROS A BORDO
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct234
Distinct (%)4.9%
Missing219
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean26.899645
Minimum0
Maximum614
Zeros866
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-05-23T12:31:42.059011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q330
95-th percentile111.9
Maximum614
Range614
Interquartile range (IQR)27

Descriptive statistics

Standard deviation44.047016
Coefficient of variation (CV)1.6374572
Kurtosis24.1706
Mean26.899645
Median Absolute Deviation (MAD)11
Skewness3.93525
Sum128661
Variance1940.1397
MonotonicityNot monotonic
2023-05-23T12:31:42.329848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 866
 
17.3%
4 170
 
3.4%
2 162
 
3.2%
5 140
 
2.8%
3 130
 
2.6%
7 130
 
2.6%
9 128
 
2.6%
10 128
 
2.6%
8 126
 
2.5%
1 119
 
2.4%
Other values (224) 2684
53.7%
(Missing) 219
 
4.4%
ValueCountFrequency (%)
0 866
17.3%
1 119
 
2.4%
2 162
 
3.2%
3 130
 
2.6%
4 170
 
3.4%
5 140
 
2.8%
6 109
 
2.2%
7 130
 
2.6%
8 126
 
2.5%
9 128
 
2.6%
ValueCountFrequency (%)
614 1
< 0.1%
509 1
< 0.1%
503 1
< 0.1%
381 1
< 0.1%
374 1
< 0.1%
364 1
< 0.1%
338 1
< 0.1%
335 1
< 0.1%
327 1
< 0.1%
316 1
< 0.1%

crew_aboard
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)0.7%
Missing217
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean4.5216301
Minimum0
Maximum83
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-05-23T12:31:42.618320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum83
Range83
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7586025
Coefficient of variation (CV)0.83124944
Kurtosis62.87587
Mean4.5216301
Median Absolute Deviation (MAD)2
Skewness4.96181
Sum21636
Variance14.127093
MonotonicityNot monotonic
2023-05-23T12:31:42.889254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3 954
19.1%
2 828
16.6%
4 694
13.9%
1 532
10.6%
5 513
10.3%
6 375
 
7.5%
7 244
 
4.9%
8 173
 
3.5%
9 115
 
2.3%
10 94
 
1.9%
Other values (24) 263
 
5.3%
(Missing) 217
 
4.3%
ValueCountFrequency (%)
0 7
 
0.1%
1 532
10.6%
2 828
16.6%
3 954
19.1%
4 694
13.9%
5 513
10.3%
6 375
 
7.5%
7 244
 
4.9%
8 173
 
3.5%
9 115
 
2.3%
ValueCountFrequency (%)
83 1
 
< 0.1%
61 1
 
< 0.1%
49 1
 
< 0.1%
43 1
 
< 0.1%
41 1
 
< 0.1%
33 1
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
27 1
 
< 0.1%
25 4
0.1%

cantidad de fallecidos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct199
Distinct (%)4.0%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean22.310773
Minimum0
Maximum583
Zeros76
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-05-23T12:31:43.162206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median11
Q325
95-th percentile85
Maximum583
Range583
Interquartile range (IQR)21

Descriptive statistics

Standard deviation35.01637
Coefficient of variation (CV)1.5694826
Kurtosis36.822466
Mean22.310773
Median Absolute Deviation (MAD)9
Skewness4.6201663
Sum111420
Variance1226.1461
MonotonicityNot monotonic
2023-05-23T12:31:43.451751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 381
 
7.6%
2 377
 
7.5%
3 363
 
7.3%
4 242
 
4.8%
5 234
 
4.7%
6 176
 
3.5%
7 160
 
3.2%
10 159
 
3.2%
13 132
 
2.6%
9 128
 
2.6%
Other values (189) 2642
52.8%
ValueCountFrequency (%)
0 76
 
1.5%
1 381
7.6%
2 377
7.5%
3 363
7.3%
4 242
4.8%
5 234
4.7%
6 176
3.5%
7 160
3.2%
8 128
 
2.6%
9 128
 
2.6%
ValueCountFrequency (%)
583 1
< 0.1%
520 1
< 0.1%
349 1
< 0.1%
346 1
< 0.1%
329 1
< 0.1%
301 1
< 0.1%
298 1
< 0.1%
290 1
< 0.1%
275 1
< 0.1%
271 1
< 0.1%

passenger_fatalities
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct190
Distinct (%)4.0%
Missing233
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean18.956385
Minimum0
Maximum560
Zeros1037
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-05-23T12:31:43.824682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q321
95-th percentile81
Maximum560
Range560
Interquartile range (IQR)20

Descriptive statistics

Standard deviation34.07517
Coefficient of variation (CV)1.7975564
Kurtosis36.929095
Mean18.956385
Median Absolute Deviation (MAD)8
Skewness4.6447956
Sum90403
Variance1161.1172
MonotonicityNot monotonic
2023-05-23T12:31:44.143781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1037
20.7%
1 307
 
6.1%
2 263
 
5.3%
3 193
 
3.9%
4 185
 
3.7%
5 139
 
2.8%
6 133
 
2.7%
7 126
 
2.5%
8 126
 
2.5%
9 118
 
2.4%
Other values (180) 2142
42.8%
(Missing) 233
 
4.7%
ValueCountFrequency (%)
0 1037
20.7%
1 307
 
6.1%
2 263
 
5.3%
3 193
 
3.9%
4 185
 
3.7%
5 139
 
2.8%
6 133
 
2.7%
7 126
 
2.5%
8 126
 
2.5%
9 118
 
2.4%
ValueCountFrequency (%)
560 1
< 0.1%
505 1
< 0.1%
335 1
< 0.1%
316 1
< 0.1%
307 1
< 0.1%
287 1
< 0.1%
283 1
< 0.1%
278 1
< 0.1%
258 1
< 0.1%
257 1
< 0.1%

crew_fatalities
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct28
Distinct (%)0.6%
Missing233
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean3.5890124
Minimum0
Maximum43
Zeros398
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-05-23T12:31:44.615681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile9
Maximum43
Range43
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1775106
Coefficient of variation (CV)0.88534402
Kurtosis12.868381
Mean3.5890124
Median Absolute Deviation (MAD)2
Skewness2.4992005
Sum17116
Variance10.096574
MonotonicityNot monotonic
2023-05-23T12:31:44.883634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2 892
17.8%
3 824
16.5%
1 770
15.4%
4 591
11.8%
5 401
8.0%
0 398
8.0%
6 273
 
5.5%
7 171
 
3.4%
8 130
 
2.6%
9 87
 
1.7%
Other values (18) 232
 
4.6%
(Missing) 233
 
4.7%
ValueCountFrequency (%)
0 398
8.0%
1 770
15.4%
2 892
17.8%
3 824
16.5%
4 591
11.8%
5 401
8.0%
6 273
 
5.5%
7 171
 
3.4%
8 130
 
2.6%
9 87
 
1.7%
ValueCountFrequency (%)
43 1
 
< 0.1%
33 1
 
< 0.1%
27 1
 
< 0.1%
25 2
 
< 0.1%
23 6
0.1%
22 5
0.1%
21 2
 
< 0.1%
20 3
0.1%
19 5
0.1%
18 3
0.1%

ground
Real number (ℝ)

SKEWED  ZEROS 

Distinct51
Distinct (%)1.0%
Missing44
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1.7204518
Minimum0
Maximum2750
Zeros4710
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-05-23T12:31:45.237571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.15
Maximum2750
Range2750
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.529088
Coefficient of variation (CV)32.275876
Kurtosis2420.877
Mean1.7204518
Median Absolute Deviation (MAD)0
Skewness48.957163
Sum8530
Variance3083.4796
MonotonicityNot monotonic
2023-05-23T12:31:45.776476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4710
94.2%
1 63
 
1.3%
2 34
 
0.7%
3 21
 
0.4%
4 16
 
0.3%
5 12
 
0.2%
7 10
 
0.2%
8 9
 
0.2%
10 6
 
0.1%
6 6
 
0.1%
Other values (41) 71
 
1.4%
(Missing) 44
 
0.9%
ValueCountFrequency (%)
0 4710
94.2%
1 63
 
1.3%
2 34
 
0.7%
3 21
 
0.4%
4 16
 
0.3%
5 12
 
0.2%
6 6
 
0.1%
7 10
 
0.2%
8 9
 
0.2%
9 1
 
< 0.1%
ValueCountFrequency (%)
2750 2
< 0.1%
225 1
< 0.1%
125 2
< 0.1%
113 1
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
78 1
< 0.1%
71 1
< 0.1%
63 1
< 0.1%
58 1
< 0.1%

summary
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct4851
Distinct (%)98.1%
Missing59
Missing (%)1.2%
Memory size1.4 MiB
Crashed under unknown circumstances.
 
9
Crashed while en route.
 
8
Crashed while attempting to land.
 
7
Crashed during takeoff.
 
6
Crashed into the sea.
 
5
Other values (4846)
4908 

Length

Max length2669
Median length787
Mean length223.39794
Min length8

Characters and Unicode

Total characters1104256
Distinct characters101
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4807 ?
Unique (%)97.2%

Sample

1st rowCrashed into trees while attempting to land after being shot down by British and French aircraft.
2nd rowExploded and burned near Neuwerk Island, when hydrogen gas, being vented, was ignited by lightning.
3rd rowCrashed near the Black Sea, cause unknown.
4th rowShot down by British aircraft crashing in flames.
5th rowShot down in flames by the British 39th Home Defence Squadron.

Common Values

ValueCountFrequency (%)
Crashed under unknown circumstances. 9
 
0.2%
Crashed while en route. 8
 
0.2%
Crashed while attempting to land. 7
 
0.1%
Crashed during takeoff. 6
 
0.1%
Crashed into the sea. 5
 
0.1%
Crashed shortly after taking off. 5
 
0.1%
Crashed on takeoff. 4
 
0.1%
Crashed under unknown circumstances 4
 
0.1%
Crashed en route. 4
 
0.1%
Shot down by rebel forces. 4
 
0.1%
Other values (4841) 4887
97.7%
(Missing) 59
 
1.2%

Length

2023-05-23T12:31:46.130418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 18449
 
10.1%
of 5538
 
3.0%
a 5446
 
3.0%
and 5436
 
3.0%
to 5425
 
3.0%
in 3675
 
2.0%
crashed 3386
 
1.9%
was 2773
 
1.5%
aircraft 2556
 
1.4%
into 2356
 
1.3%
Other values (11551) 127814
69.9%

Most occurring characters

ValueCountFrequency (%)
179143
16.2%
e 104790
 
9.5%
t 81829
 
7.4%
a 79836
 
7.2%
n 68037
 
6.2%
i 65785
 
6.0%
r 63354
 
5.7%
o 62537
 
5.7%
h 42752
 
3.9%
s 39752
 
3.6%
Other values (91) 316441
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 868368
78.6%
Space Separator 179150
 
16.2%
Uppercase Letter 25255
 
2.3%
Other Punctuation 20590
 
1.9%
Decimal Number 8833
 
0.8%
Dash Punctuation 1642
 
0.1%
Close Punctuation 158
 
< 0.1%
Open Punctuation 140
 
< 0.1%
Final Punctuation 67
 
< 0.1%
Control 33
 
< 0.1%
Other values (4) 20
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 104790
12.1%
t 81829
 
9.4%
a 79836
 
9.2%
n 68037
 
7.8%
i 65785
 
7.6%
r 63354
 
7.3%
o 62537
 
7.2%
h 42752
 
4.9%
s 39752
 
4.6%
d 38361
 
4.4%
Other values (30) 221335
25.5%
Uppercase Letter
ValueCountFrequency (%)
T 5791
22.9%
C 2773
11.0%
A 2576
10.2%
S 1527
 
6.0%
F 1284
 
5.1%
M 1206
 
4.8%
I 1062
 
4.2%
P 960
 
3.8%
W 922
 
3.7%
N 860
 
3.4%
Other values (16) 6294
24.9%
Other Punctuation
ValueCountFrequency (%)
. 13466
65.4%
, 5709
27.7%
' 770
 
3.7%
" 362
 
1.8%
/ 170
 
0.8%
: 56
 
0.3%
; 34
 
0.2%
& 17
 
0.1%
% 3
 
< 0.1%
# 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 2661
30.1%
1 1365
15.5%
2 1040
 
11.8%
5 827
 
9.4%
3 819
 
9.3%
4 577
 
6.5%
6 431
 
4.9%
7 415
 
4.7%
8 386
 
4.4%
9 312
 
3.5%
Space Separator
ValueCountFrequency (%)
179143
> 99.9%
  7
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 157
99.4%
] 1
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 139
99.3%
[ 1
 
0.7%
Control
ValueCountFrequency (%)
32
97.0%
1
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 1642
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 67
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 7
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7
100.0%
Other Symbol
ValueCountFrequency (%)
° 3
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 893623
80.9%
Common 210633
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 104790
11.7%
t 81829
 
9.2%
a 79836
 
8.9%
n 68037
 
7.6%
i 65785
 
7.4%
r 63354
 
7.1%
o 62537
 
7.0%
h 42752
 
4.8%
s 39752
 
4.4%
d 38361
 
4.3%
Other values (56) 246590
27.6%
Common
ValueCountFrequency (%)
179143
85.0%
. 13466
 
6.4%
, 5709
 
2.7%
0 2661
 
1.3%
- 1642
 
0.8%
1 1365
 
0.6%
2 1040
 
0.5%
5 827
 
0.4%
3 819
 
0.4%
' 770
 
0.4%
Other values (25) 3191
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1104114
> 99.9%
None 72
 
< 0.1%
Punctuation 70
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
179143
16.2%
e 104790
 
9.5%
t 81829
 
7.4%
a 79836
 
7.2%
n 68037
 
6.2%
i 65785
 
6.0%
r 63354
 
5.7%
o 62537
 
5.7%
h 42752
 
3.9%
s 39752
 
3.6%
Other values (73) 316299
28.6%
Punctuation
ValueCountFrequency (%)
’ 67
95.7%
‘ 3
 
4.3%
None
ValueCountFrequency (%)
é 20
27.8%
á 15
20.8%
í 8
 
11.1%
  7
 
9.7%
ó 3
 
4.2%
° 3
 
4.2%
ö 3
 
4.2%
ü 2
 
2.8%
ð 2
 
2.8%
ã 2
 
2.8%
Other values (6) 7
 
9.7%

year
Real number (ℝ)

Distinct107
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.923
Minimum1915
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-05-23T12:31:46.736309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1915
5-th percentile1931
Q11951
median1970
Q31992
95-th percentile2010
Maximum2021
Range106
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.632185
Coefficient of variation (CV)0.012497791
Kurtosis-0.96532199
Mean1970.923
Median Absolute Deviation (MAD)20
Skewness-0.024044307
Sum9858557
Variance606.74452
MonotonicityIncreasing
2023-05-23T12:31:47.419188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1946 88
 
1.8%
1989 83
 
1.7%
1947 82
 
1.6%
1948 78
 
1.6%
1962 78
 
1.6%
1972 77
 
1.5%
1945 75
 
1.5%
1951 75
 
1.5%
1994 74
 
1.5%
1970 73
 
1.5%
Other values (97) 4219
84.3%
ValueCountFrequency (%)
1915 2
 
< 0.1%
1916 5
 
0.1%
1917 7
 
0.1%
1918 4
 
0.1%
1919 9
0.2%
1920 18
0.4%
1921 12
0.2%
1922 13
0.3%
1923 13
0.3%
1924 7
 
0.1%
ValueCountFrequency (%)
2021 7
 
0.1%
2020 8
 
0.2%
2019 13
0.3%
2018 19
0.4%
2017 15
0.3%
2016 23
0.5%
2015 18
0.4%
2014 23
0.5%
2013 25
0.5%
2012 26
0.5%

Interactions

2023-05-23T12:31:34.114763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:19.759145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:21.866532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:23.935323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:25.801782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:28.034152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:30.204767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:32.150264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:34.365719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:20.025854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:22.156481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:24.176650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:26.052720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:28.314104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:30.485720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:32.402205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:34.626671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:20.297808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:22.409436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:24.413628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:26.322672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:28.682052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:30.748621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:32.693047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:34.855023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:20.553762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:22.684541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:24.636573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:26.715608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:28.904995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:30.973583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:32.913980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:35.109994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:20.811716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:22.960494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:24.876531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:26.963355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:29.161952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:31.231538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:33.167946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:35.348621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:21.078669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:23.209448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:25.111490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:27.223308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:29.388910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:31.465499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:33.414884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:35.576581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:21.335625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:23.445410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:25.341446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:27.471249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:29.635869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:31.681330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:33.638846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:36.021493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:21.598597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:23.690368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:25.569426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:27.727205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:29.901820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:31.907290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-23T12:31:33.872807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-23T12:31:47.664308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
all_aboardPASAJEROS A BORDOcrew_aboardcantidad de fallecidospassenger_fatalitiescrew_fatalitiesgroundyear
all_aboard1.0000.9660.6670.7450.7810.3680.0380.171
PASAJEROS A BORDO0.9661.0000.5030.7080.8190.2320.0200.165
crew_aboard0.6670.5031.0000.5210.3790.6890.0990.109
cantidad de fallecidos0.7450.7080.5211.0000.9400.680-0.0070.110
passenger_fatalities0.7810.8190.3790.9401.0000.457-0.0250.109
crew_fatalities0.3680.2320.6890.6800.4571.0000.0410.041
ground0.0380.0200.099-0.007-0.0250.0411.0000.057
year0.1710.1650.1090.1100.1090.0410.0571.000

Missing values

2023-05-23T12:31:36.458410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-23T12:31:36.998025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-23T12:31:37.627418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

fechaRutaOperadORrouteac_typeall_aboardPASAJEROS A BORDOcrew_aboardcantidad de fallecidospassenger_fatalitiescrew_fatalitiesgroundsummaryyear
61915-03-05Tienen, BelgiumMilitary - German NavyNaNZeppelin L-8 (airship)41.00.041.017.00.017.00.0Crashed into trees while attempting to land after being shot down by British and French aircraft.1915
71915-09-03Off Cuxhaven, GermanyMilitary - German NavyNaNZeppelin L-10 (airship)19.0NaNNaN19.0NaNNaN0.0Exploded and burned near Neuwerk Island, when hydrogen gas, being vented, was ignited by lightning.1915
81916-07-28Near Jambol, BulgeriaMilitary - German ArmyNaNSchutte-Lanz S-L-10 (airship)20.0NaNNaN20.0NaNNaN0.0Crashed near the Black Sea, cause unknown.1916
91916-09-24Billericay, EnglandMilitary - German NavyNaNZeppelin L-32 (airship)22.0NaNNaN22.0NaNNaN0.0Shot down by British aircraft crashing in flames.1916
101916-10-01Potters Bar, EnglandMilitary - German NavyNaNZeppelin L-31 (airship)19.00.019.019.00.019.00.0Shot down in flames by the British 39th Home Defence Squadron.1916
111916-11-21Mainz, GermanyMilitary - German ArmyNaNSuper Zeppelin (airship)28.0NaNNaN27.0NaNNaN0.0Crashed in a storm.1916
121916-11-28Off West Hartlepool, EnglandMilitary - German NavyNaNZeppelin L-34 (airship)20.0NaNNaN20.0NaNNaN0.0Shot down by British anti-aircraft fire and aircraft and crashed into the North Sea.1916
131917-03-04Near Gent, BelgiumMilitary - German ArmyNaNAirship20.0NaNNaN20.0NaNNaN0.0Caught fire and crashed.1917
141917-03-30Off Northern GermanyMilitary - German NavyNaNSchutte-Lanz S-L-9 (airship)23.0NaNNaN23.0NaNNaN0.0Struck by lightning and crashed into the Baltic Sea.1917
151917-05-14Near Texel Island, North SeaMilitary - German NavyNaNZeppelin L-22 (airship)21.0NaNNaN21.0NaNNaN0.0Crashed into the sea from an altitude of 3,000 ft. after being hit by British aircraft fire.1917
fechaRutaOperadORrouteac_typeall_aboardPASAJEROS A BORDOcrew_aboardcantidad de fallecidospassenger_fatalitiescrew_fatalitiesgroundsummaryyear
49982020-08-07Calicut, IndiaAir India ExppressDubai - CalicutBoeing 737-8HG190.0184.06.020.018.02.00.0The flight IX344 suffered a runway excursion while landing at Kozhikode-Calicut Airport in heavy rain. The nose section separated from the fuselage after going down a steep slope at the end of the runway. The pilot and copilot were among the dead. Low visibility, wet runway, low cloud base and poor braking action possibly contributed to the accident.2020
49992020-08-22Juba, South SudanSouth West AviaitonJuba - WauAntonov 26B8.05.03.07.04.03.00.0The cargo plane lost height shortly after departure from Juba Airport and impacted a farm near Hai Referendum about 3nm southwest of the airport. One passenger survived in critical condition. The plane was chartered by the World Food Program to transport supplies and wages to Wau and Aweil.2020
50002020-09-25Near Chuguev, UkraineMilitary - Ukraine Air ForceTrainingAntonov An26SH27.020.07.026.019.07.00.0The military transport, crashed 1.2 miles from Chuguev air base. The plane was carrying cadets from a nearby air force university on a training flight. The crew may have reported failure of an engine prior to the accident.2020
50012021-01-09Near Jakarta, IndonesiaSriwijaya AirJakarta - PontianakBoeing 737-52462.056.06.062.056.06.00.0Sriwijaya Air flight 182 was climbing through 10,900 ft., 11 nm north of Jakarta-Soekarno-Hatta International Airport, over the Java Sea when radar and radio contact was lost. The aircraft then lost height rapidly and impacted the Java Sea. Debris was located near Lancang Island.2021
50022021-03-02Pieri, SudanSouth Sudan Supreme AirlinesPieri - YuaiLet L-410UVP-E10.08.02.010.08.02.00.0One of the engines on the aircraft failed 10 minutes after takeof. When the plane turned back, the second engine failed.2021
50032021-03-28Near Butte, AlaskaSoloy HelicoptersSightseeing CharterEurocopter AS350B3 Ecureuil6.05.01.05.04.01.00.0The sightseeing helicopter crashed after missing the top of a 6,000 ft mountain by just 10 - 15 ft. The crash site was near Knik glacier. The pilot, and four others were killed including Czech billionaire Petr Kellner.2021
50042021-05-21Near Kaduna, NigeriaMilitary - Nigerian Air ForceNaNBeechcraft B300 King Air 350i11.07.04.011.07.04.00.0While on final approach, in poor weather conditions, the aircraft crashed and burst into flames less than 10 km from Kaduna Airport. All 11 occupants were killed, incuding General Ibrahim Attahiru, Chief of Staff of the Nigerian Army.2021
50052021-06-10Near Pyin Oo Lwin, MyanmarMilitary - Myanmar Air ForceNaypyidaw - AnisakanBeechcraft 1900D14.012.02.012.011.01.00.0The plane was carrying military personnel and monks when it crashed about 300 meters from a steel plant in the Mandalay region. The plane was attempting to land in poor weather conditions and broke into three pieces.2021
50062021-07-04Patikul, Sulu, PhilippinesMilitary - Philippine Air ForceCagayan de Oro-Lumbia - JoloLockheed C-130H Hercules96.088.08.050.0NaNNaN3.0While attempting to land at Jolo Airport, the military transport overran the runway, struck two houses and burst into flames coming to rest on a coconut plantation.2021
50072021-07-06Palana, RussiaKamchatka Aviation EnterprisePetropavlovsk - PalanaAntonov An 26B-10028.022.06.028.022.06.00.0The passenger plane crashed into the top of a cliff while attempting to land in inclement weather. The debris fell into the sea. Contact was lost with the plane 10 minutes before it was to land.2021